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README.md

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+---
+license: agpl-3.0
+pipeline_tag: keypoint-detection
+library_name: yolov26
+---
+
+# YOLO26-S-pose
+
+Ultralytics YOLO26 is the latest evolution in the YOLO series, engineered from the ground up for edge and low-power devices. This is the **pose estimation** variant optimized for human keypoint detection.
+
+## Model Specifications
+
+| Property | Value |
+|----------|-------|
+| Input Size | 640 pixels |
+| mAP Pose (50-95, e2e) | 63.0 |
+| mAP Pose (50, e2e) | 86.6 |
+| CPU Speed (ONNX) | 85.3 ms |
+| T4 TensorRT10 Speed | 2.7 ms |
+| Parameters | 10.4M |
+| FLOPs | 23.9B |
+
+## Key Features
+
+The architecture of YOLO26 is guided by three core principles:
+
+**Simplicity:** YOLO26 is a native end-to-end model, producing predictions directly without the need for non-maximum suppression (NMS). By eliminating this post-processing step, inference becomes faster, lighter, and easier to deploy in real-world systems.
+
+**Deployment Efficiency:** The end-to-end design cuts out an entire stage of the pipeline, dramatically simplifying integration, reducing latency, and making deployment more robust across diverse environments.
+
+**Training Innovation:** YOLO26 introduces the MuSGD optimizer, a hybrid of SGD and Muon — inspired by Moonshot AI's Kimi K2 breakthroughs in LLM training. This optimizer brings enhanced stability and faster convergence.
+
+### Precision Pose Estimation Features
+
+- **Residual Log-Likelihood Estimation (RLE):** More accurate keypoint localization
+- **Optimized Decoding:** Increased inference speed for pose estimation
+- **DFL Removal:** Simplified inference and broader hardware compatibility
+- **Up to 43% Faster CPU Inference:** Optimized for edge computing
+
+## Usage
+
+Install ultralytics with `pip install ultralytics`.
+
+Download the model.
+```python
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(repo_id="openvision/yolo26-s-pose", filename="model.pt")
+```
+
+Infer.
+```python
+from ultralytics import YOLO
+from PIL import Image
+import requests
+
+model = YOLO(model_path)
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Run inference with the YOLO26s-pose model on the image
+results = model.predict(image)
+```
+
+## Documentation
+
+For more information, see the [official YOLO26 documentation](https://docs.ultralytics.com/models/yolo26/).

model.pt

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